Harmonizing Plasmonic and Photonic Effects to Boost Photocatalytic H2 Production over 550 mmol ⋠h-1 ⋠gcat -1.

Integrating plasmonic nanoparticles with photonic crystals holds immense potential to enhance green hydrogen photosynthesis by amplifying localized electromagnetic field through generating surface plasmons and slow photons. Current plasmonic photonic designs primarily employ semiconductor-based structural backbone deposited with plasmonic nanoparticles. However, the competition between various optical phenomena in these ensembles hinders effective field enhancement rather than facilitating it. This limitation creates a formidable performance bottleneck that retards hydrogen evolution. Herein, we enhance plasmonic catalysis for efficient hydrogen evolution by effectively harmonizing plasmonic and photonic effects. This is achieved by using inert SiO2 opal as a non-photoabsorbing photonic framework. By aligning the excitation wavelengths of surface plasmons and slow photons, our optimized plasmonic photonic crystals demonstrates a remarkable H2 evolution rate of 560 mmol h-1 gAg -1, surpassing bare plasmonic Ag nanoparticles by >106-fold and other high-performance photocatalytic designs by 280-fold. Mechanistic studies highlight the pivotal role of the non-photoabsorbing photonic backbone in facilitating effective light confinement through the photonic effect. This in turn boosts the plasmonic field for enhanced photocatalytic H2 evolution, even without needing additional co-catalysts. Our work offers valuable insights for future design of electromagnetically hot plasmonic catalysts to achieve efficient light-to-chemical transformations in diverse energy, chemical, and environmental applications.

Catalyst-On-Hotspot Nanoarchitecture: Plasmonic Focusing of Light onto Co-Photocatalyst for Efficient Light-To-Chemical Transformation.

Plasmon-mediated catalysis utilizing hybrid photocatalytic ensembles promises effective light-to-chemical transformation, but current approaches suffer from weak electromagnetic field enhancements from polycrystalline and isotropic plasmonic nanoparticles as well as poor utilization of precious co-catalyst. Here, efficient plasmon-mediated catalysis is achieved by introducing a unique catalyst-on-hotspot nanoarchitecture obtained through the strategic positioning of co-photocatalyst onto plasmonic hotspots to concentrate light energy directly at the point-of-reaction. Using environmental remediation as a proof-of-concept application, the catalyst-on-hotspot design (edge-AgOcta@Cu2O) enhances photocatalytic advanced oxidation processes to achieve superior organic-pollutant degradation at ≈81% albeit having lesser Cu2O co-photocatalyst than the fully deposited design (full-AgOcta@Cu2O). Mass-normalized rate constants of edge-AgOcta@Cu2O reveal up to 20-fold and 3-fold more efficient utilization of Cu2O and Ag nanoparticles, respectively, compared to full-AgOcta@Cu2O and standalone catalysts. Moreover, this design also exhibits catalytic performance >4-fold better than emerging hybrid photocatalytic platforms. Mechanistic studies unveil that the light-concentrating effect facilitated by the dense electromagnetic hotspots is crucial to promote the generation and utilization of energetic photocarriers for enhanced catalysis. By enabling the plasmonic focusing of light onto co-photocatalyst at the single-particle level, the unprecedented design offers valuable insights in enhancing light-driven chemical reactions and realizing efficient energy/catalyst utilizations for diverse chemical, environmental, and energy applications.

Chaotropic Nanoelectrocatalysis: Chemically Disrupting Water Intermolecular Network at the Point-of-Catalysis to Boost Green Hydrogen Electrosynthesis.

Efficient green hydrogen production through electrocatalytic water splitting serves as a powerful catalyst for realizing a carbon-free hydrogen economy. However, current electrocatalytic designs face challenges such as poor hydrogen evolution reaction (HER) performance (Tafel slope, 100-140 mV dec-1 ) because water molecules are thermodynamically trapped within their extensive hydrogen bonding network. Herein, we drive efficient HER by manipulating the local water microenvironment near the electrocatalyst. This is achieved by functionalizing the nanoelectrocatalyst's surface with a monolayer of chaotropic molecules to chemically weaken water-water interactions directly at the point-of-catalysis. Notably, our chaotropic design demonstrates a superior Tafel slope (77 mV dec-1 ) and the lowest overpotential (0.3 V at 10 mA cm-2 ECSA ), surpassing its kosmotropic counterparts (which reinforces the water molecular network) and previously reported electrocatalytic designs by up to ≈2-fold and ≈3-fold, respectively. Comprehensive mechanistic investigations highlight the critical role of chaotropic surface chemistry in disrupting the water intermolecular network, thereby releasing free/weakly bound water molecules that strongly interact with the electrocatalyst to boost HER. Our study provides a unique molecular approach that can be readily integrated with emerging electrocatalytic materials to rapidly advance the electrosynthesis of green hydrogen, holding immense promise for sustainable chemical and energy applications.

Incorporating Chaotropic/Kosmotropic Chemistries onto Plasmonic Nanoheater to Boost Steam Generation Beyond its Photothermal Property.

Photothermal steam generation promises decentralized water purification, but current methods suffer from slow water evaporation even at high photothermal efficiency of ≈98%. This drawback arises from the high latent heat of vaporization that is required to overcome the strong and extensive hydrogen bonding network in water for steam generation. Here, light-to-vapor conversion is boosted by incorporating chaotropic/kosmotropic chemistries onto plasmonic nanoheater to manipulate water intermolecular network at the point-of-heating. The chaotropic-plasmonic nanoheater affords rapid light-to-vapor conversion (2.79 kg m-2  h-1  kW-1 ) at ≈83% efficiency, with the steam generation rate up to 6-fold better than kosmotropic platforms or emerging photothermal designs. Notably, the chaotropic-plasmonic nanoheater also lowers the enthalpy of water vaporization by 1.6-fold when compared to bulk water, signifying that a correspondingly higher amount of steam can be generated with the same energy input. Simulation studies unveil chaotropic surface chemistry is crucial to disrupt water hydrogen bonding network and suppress the energy barrier for water evaporation. Using the chaotropic-plasmonic nanoheater, organic-polluted water is purified at ≈100% efficiency, a feat otherwise challenging in conventional treatments. This study offers a unique chemistry approach to boost light-driven steam generation beyond a material photothermal property.

Superlattice-based Plasmonic Catalysis: Concentrating Light at the Nanoscale to Drive Efficient Nitrogen-to-Ammonia Fixation at Ambient Conditions.

Plasmonic catalysis promises green ammonia synthesis but is limited by the need for co-catalysts and poor performances due to weak electromagnetic field enhancement. Here, we use two-dimensional plasmonic superlattices with dense electromagnetic hotspots to boost ambient nitrogen-to-ammonia photoconversion without needing co-catalyst. By organizing Ag octahedra into a square superlattice to concentrate light, the ammonia formation is enhanced by ≈15-fold and 4-fold over hexagonal superlattice and disorganized array, respectively. Our unique catalyst achieves superior ammonia formation rate and apparent quantum yield up to ≈15-fold and ≈103 -fold, respectively, better than traditional designs. Mechanistic investigations reveal the abundance of intense plasmonic hotspots is crucial to promote hot electron generation and transfer for nitrogen reduction. Our work offers valuable insights to design electromagnetically hot plasmonic catalysts for diverse chemical and energy applications.

Inducing Ring Complexation for Efficient Capture and Detection of Small Gaseous Molecules Using SERS for Environmental Surveillance.

Gas-phase surface-enhanced Raman scattering (SERS) remains challenging due to poor analyte affinity to SERS substrates. The reported use of capturing probes suffers from concurrent inconsistent signals and long response time due to the formation of multiple potential probe-analyte interaction orientations. Here, we demonstrate the use of multiple non-covalent interactions for ring complexation to boost the affinity of small gas molecules, SO2 and NO2 , to our SERS platform, achieving rapid capture and multiplex detection down to 100 ppm. Experimental and in-silico studies affirm stable ring complex formation, and kinetic investigations reveal a 4-fold faster response time compared to probes without stable ring complexation capability. By synergizing spectral concatenation and support vector machine regression, we achieve 91.7 % accuracy for multiplex quantification of SO2 and NO2 in excess CO2 , mimicking real-life exhausts. Our platform shows immense potential for on-site exhaust and air quality surveillance.

Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min.

Population-wide surveillance of COVID-19 requires tests to be quick and accurate to minimize community transmissions. The detection of breath volatile organic compounds presents a promising option for COVID-19 surveillance but is currently limited by bulky instrumentation and inflexible analysis protocol. Here, we design a hand-held surface-enhanced Raman scattering-based breathalyzer to identify COVID-19 infected individuals in under 5 min, achieving >95% sensitivity and specificity across 501 participants regardless of their displayed symptoms. Our SERS-based breathalyzer harnesses key variations in vibrational fingerprints arising from interactions between breath metabolites and multiple molecular receptors to establish a robust partial least-squares discriminant analysis model for high throughput classifications. Crucially, spectral regions influencing classification show strong corroboration with reported potential COVID-19 breath biomarkers, both through experiment and in silico. Our strategy strives to spur the development of next-generation, noninvasive human breath diagnostic toolkits tailored for mass screening purposes.